Abstract
Cervical spinal cord injuries frequently cause paralysis of all four limbs - a medical condition known as tetraplegia. Functional electrical stimulation (FES), when combined with an appropriate controller, can be used to restore motor function by electrically stimulating the neuromuscular system. Previous works have demonstrated that reinforcement learning can be used to successfully train FES controllers. Here, we demonstrate that transfer learning and curriculum learning can be used to improve the learning rates, accuracies, and workspaces of FES controllers that are trained using reinforcement learning.
Highlights
H IGH-level cervical spinal cord injuries (SCIs) often cause paralysis of all four limbs, resulting in decreased patient independence and quality of life
Many Functional electrical stimulation (FES) controllers have been proposed in the literature [2]–[14], including controllers based on reinforcement learning (RL) [16]
We have demonstrated several enhancements to RL algorithms that can be used to train controllers for multi-input, multi-output musculoskeletal models of the human arm
Summary
H IGH-level cervical spinal cord injuries (SCIs) often cause paralysis of all four limbs, resulting in decreased patient independence and quality of life. When paired with an appropriate command source, such as a brain computer interface [1], and a controller to coordinate electrical stimulation [2]–[14], FES systems can help people with paralysis regain some independence and quality of life [15]. An FES controller was trained using RL to move a horizontal-planar model of the human arm with 2 segments (upper and lower arm) and 6 redundant muscles to targets with radii of 7.5 cm that spawned randomly within the joint angle range [20°, 90°] for both the shoulder and elbow [9]. The results of [9] clearly demonstrated the feasibility of using RL for training FES controllers for a two-dimensional musculoskeletal model of the arm
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Schedule a callMore From: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
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